MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection
The intentional creation and spread of disinformation poses a significant threat to public discourse. However, existing English datasets and research rarely address the intentionality behind the disinformation. This work presents MALINT, the first human-annotated English corpus developed in collaboration with expert fact-checkers to capture disinformation and its malicious intent. We utilize our novel corpus to benchmark 12 language models, including small language models (SLMs) such as BERT and large language models (LLMs) like Llama 3.3, on binary and multilabel intent classification tasks. Moreover, inspired by inoculation theory from psychology and communication studies, we investigat...
| Authors | Arkadiusz Modzelewski, Witold Sosnowski, Eleni Papadopulos et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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